19 Hours


This Machine Learning course provides participants a robust grounding in fundamental machine learning principles and practical skills. This foundational knowledge is a gateway for deeper exploration into the captivating domains of artificial intelligence and data science. Upon course completion, learners will possess proficiency in data preprocessing, insightful data analysis, resilient model construction, and meticulous model evaluation—a skill set indispensable for making informed, data-driven decisions. Whether a learner is a newcomer or a professional looking to enhance their understanding of machine learning, this micro-credential course offers a structured and comprehensive path to success.

Course Outline

Module 1: Introduction to Data for Machine Learning

Module 2: Explore and Analyze Data with Python

Module 3: Train and Understand Regression Models in Machine Learning

Module 4: Refine and Test Machine Learning Models

Module 5: Train and Evaluate Regression Models

Module 6: Create and Understand Classification Models in Machine Learning

Module 7: Customize Architectures and Hyperparameters Using Random Forest

Module 8: Confusion Matrix and Data Imbalances

Module 9: Optimize Model Performance with ROC and AUC

Module 10: Train and Evaluate Classification Models

Module 11: Train and Evaluate Clustering Models

Module 12: Train and Evaluate Deep Learning Models

Required Resources

Laptop, Intel Core i5 or higher, 16GB, 1TB Storage, Graphics Card (Hardware); Microsoft Azure (Software); Adequate Internet Connection (Network)


This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.


In this Machine Learning course, there will be

  • One (1) diagnostic assessment conducted synchronously, and is knowledge-based, with the flexibility for learners to choose between remote or on-site participation.
  • Two (2) formative assessments, one focused on knowledge and the other on performance. Both are available asynchronously, allowing learners to complete them at their own pace, and learners have the option to participate remotely or on-site.
  • A performance-based summative assessment conducted synchronously, providing learners with the choice of remote or on-site participation.

Credit and Recognition

Upon successful completion of the Machine Learning course, learners will receive a Certificate of Completion and a badge. These serve both as a recognition of acquired expertise in machine learning and as a foundational step toward more advanced studies in Data Science.

This course is facilitated by a Microsoft Certified Professional, someone who has completed professional training for Microsoft products through a certification program provided by Microsoft. To ensure the quality of this micro-credential, continuous feedback loops with students, instructors, and industry practitioners are maintained to improve content, delivery, and assessment methods continuously.

Learning Pathways

Machine Learning Path:

Your journey into Machine Learning begins with either Data Engineering or Data Management. This foundational knowledge will equip you with the skills needed to explore the exciting field of Machine Learning, which, in turn, opens doors to the broader field of Data Science.

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